10 papers
BenthicFlow: Generating Extensible Underwater Environments via Flow Matching
Joaquín Figueira, Camile Lendering, Manfred Gonzalez-Hernandez +3
Computer vision applications for 3D scene understanding in underwater environments remain challenging due to the lack of high-quality 3D data and the inability of surface-trained m…
Localizing to Debias: A Patch-Level Benchmark and Baseline for Weakly Supervised Spatial Anomaly Detection
Sara Abdulaziz, Abdulrahman Al-Abri, Giacomo D'Amicantonio +1
Despite growing interest in weakly supervised video anomaly detection (WSVAD), current methods struggle to bridge the gap between coarse temporal supervision and fine-grained spati…
AOI-SSL: Self-Supervised Framework for Efficient Segmentation of Wire-bonded Semiconductors In Optical Inspection
Joaquín Figueira, Rob Van Gastel, Giacomo D'Amicantonio +4
Segmentation models in automated optical inspection of wire-bonded semiconductors are typically device-specific and must be re-trained when new devices or distribution shifts appea…
Scaling Self-Supervised and Cross-Modal Pretraining for Volumetric CT Transformers
Cris Claessens, Christiaan Viviers, Giacomo D'Amicantonio +2
We introduce SPECTRE, a fully transformer-based foundation model for volumetric computed tomography (CT). Our Self-Supervised & Cross-Modal Pretraining for CT Representation Extrac…
Mixture of Experts Guided by Gaussian Splatters Matters: A new Approach to Weakly-Supervised Video Anomaly Detection
Giacomo D'Amicantonio, Snehashis Majhi, Quan Kong +4
Video Anomaly Detection (VAD) is a challenging task due to the variability of anomalous events and the limited availability of labeled data. Under the Weakly-Supervised VAD (WSVAD)…
Evaluation of Human Visual Privacy Protection: A Three-Dimensional Framework and Benchmark Dataset
Sara Abdulaziz, Giacomo D'Amicantonio, Egor Bondarev
Recent advances in AI-powered surveillance have intensified concerns over the collection and processing of sensitive personal data. In response, research has increasingly focused o…
Just Dance with ! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection
Snehashis Majhi, Giacomo D'Amicantonio, Antitza Dantcheva +5
Weakly-supervised methods for video anomaly detection (VAD) are conventionally based merely on RGB spatio-temporal features, which continues to limit their reliability in real-worl…
uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories
Giacomo D'Amicantonio, Egor Bondarau, Peter H. N. de With
Deep learning-based approaches have achieved significant improvements on public video anomaly datasets, but often do not perform well in real-world applications. This paper address…
Automated Camera Calibration via Homography Estimation with GNNs
Giacomo D'Amicantonio, Egor Bondarev, Peter H. N. De With
Over the past few decades, a significant rise of camera-based applications for traffic monitoring has occurred. Governments and local administrations are increasingly relying on th…
Homography Estimation in Complex Topological Scenes
Giacomo D'Amicantonio, Egor Bondarau, Peter H. N. De With
Surveillance videos and images are used for a broad set of applications, ranging from traffic analysis to crime detection. Extrinsic camera calibration data is important for most a…